{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<!--NAVIGATION-->\n",
    "< [Feature Engineering](05.04-Feature-Engineering.ipynb) | [Contents](Index.ipynb) | [In Depth: Linear Regression](05.06-Linear-Regression.ipynb) >\n",
    "\n",
    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/05.05-Naive-Bayes.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# In Depth: Naive Bayes Classification\n",
    "\n",
    "# 深入：朴素贝叶斯分类"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> The previous four sections have given a general overview of the concepts of machine learning.\n",
    "In this section and the ones that follow, we will be taking a closer look at several specific algorithms for supervised and unsupervised learning, starting here with naive Bayes classification.\n",
    "\n",
    "前面四个小节对机器学习的概念给出了概述。本节开始，我们会进入到有监督学习和无监督学习的一些特定算法当中，进行较深入的介绍。首先从本节的朴素贝叶斯分来开始。\n",
    "\n",
    "> Naive Bayes models are a group of extremely fast and simple classification algorithms that are often suitable for very high-dimensional datasets.\n",
    "Because they are so fast and have so few tunable parameters, they end up being very useful as a quick-and-dirty baseline for a classification problem.\n",
    "This section will focus on an intuitive explanation of how naive Bayes classifiers work, followed by a couple examples of them in action on some datasets.\n",
    "\n",
    "朴素贝叶斯模型是一组非常快和简单的分类算法，它们经常用来对高维度数据集进行分类处理。因为它们非常快和有一些可调的参数，它们最终成为了分类问题很好用的临时基线方法。本节会聚焦在对朴素贝叶斯分类器工作原理的直观介绍，然后会在不同的数据集上应用它作为例子。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bayesian Classification\n",
    "\n",
    "## 贝叶斯分类\n",
    "\n",
    "> Naive Bayes classifiers are built on Bayesian classification methods.\n",
    "These rely on Bayes's theorem, which is an equation describing the relationship of conditional probabilities of statistical quantities.\n",
    "In Bayesian classification, we're interested in finding the probability of a label given some observed features, which we can write as $P(L~|~{\\rm features})$.\n",
    "Bayes's theorem tells us how to express this in terms of quantities we can compute more directly:\n",
    "\n",
    "朴素贝叶斯分类建立在贝叶斯分类方法的基础上。这些分类方法的基础是贝叶斯定理，这是一个用来描述统计理论中条件概率的等式。在贝叶斯分类中，我们感兴趣的是在给定观测特征数据上找到一个标签的概率，我们写做$P(L~|~{\\rm features})$。贝叶斯定理告诉我们如何使用这些已知的特征量直接计算概率：\n",
    "\n",
    "$$\n",
    "P(L~|~{\\rm features}) = \\frac{P({\\rm features}~|~L)P(L)}{P({\\rm features})}\n",
    "$$\n",
    "\n",
    "> If we are trying to decide between two labels—let's call them $L_1$ and $L_2$—then one way to make this decision is to compute the ratio of the posterior probabilities for each label:\n",
    "\n",
    "如果我们尝试在两个标签中去选择，假设我们称它们为$L_1$和$L_2$，那么做这个选择的一种方法是计算每一个标签的后验概率：\n",
    "\n",
    "$$\n",
    "\\frac{P(L_1~|~{\\rm features})}{P(L_2~|~{\\rm features})} = \\frac{P({\\rm features}~|~L_1)}{P({\\rm features}~|~L_2)}\\frac{P(L_1)}{P(L_2)}\n",
    "$$\n",
    "\n",
    "> All we need now is some model by which we can compute $P({\\rm features}~|~L_i)$ for each label.\n",
    "Such a model is called a *generative model* because it specifies the hypothetical random process that generates the data.\n",
    "Specifying this generative model for each label is the main piece of the training of such a Bayesian classifier.\n",
    "The general version of such a training step is a very difficult task, but we can make it simpler through the use of some simplifying assumptions about the form of this model.\n",
    "\n",
    "因此我们所需要的就是一个能够计算每一个标签的$P({\\rm features}~|~L_i)$值的模型。这个模型被称为*生成模型*，因为它指定了产生数据的假定水机过程。对于训练贝叶斯分类器来说，为每个标签找到这样的通用模型是最主要的步骤。这样的训练步骤的通用版本是很困难的，但是我们能够通过使用关于该模型的简化假设来简化这项任务。\n",
    "\n",
    "> This is where the \"naive\" in \"naive Bayes\" comes in: if we make very naive assumptions about the generative model for each label, we can find a rough approximation of the generative model for each class, and then proceed with the Bayesian classification.\n",
    "Different types of naive Bayes classifiers rest on different naive assumptions about the data, and we will examine a few of these in the following sections.\n",
    "\n",
    "这就是“朴素贝叶斯”中的“朴素”的由来：如果我们对通用模型中的每个标签作出非常朴素的假设，我们就可以找到通用模型中每个标签的大概分布，然后进行贝叶斯分类。不同的朴素贝叶斯分类器取决于对数据不同的朴素假设上，我们在本节后续内容中会介绍它们中的一部分。\n",
    "\n",
    "> We begin with the standard imports:\n",
    "\n",
    "首先是需要用到的包："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns; sns.set()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Gaussian Naive Bayes\n",
    "\n",
    "## 高斯朴素贝叶斯\n",
    "\n",
    "> Perhaps the easiest naive Bayes classifier to understand is Gaussian naive Bayes.\n",
    "In this classifier, the assumption is that *data from each label is drawn from a simple Gaussian distribution*.\n",
    "Imagine that you have the following data:\n",
    "\n",
    "朴素贝叶斯分类器中最容易理解的也许就是高斯朴素贝叶斯。这个分类器假定*每个标签的数据都服从简单正态分布*。例如你有如下数据："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.datasets import make_blobs\n",
    "X, y = make_blobs(100, 2, centers=2, random_state=2, cluster_std=1.5)\n",
    "plt.scatter(X[:, 0], X[:, 1], c=y, s=50, cmap='RdBu');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> One extremely fast way to create a simple model is to assume that the data is described by a Gaussian distribution with no covariance between dimensions.\n",
    "This model can be fit by simply finding the mean and standard deviation of the points within each label, which is all you need to define such a distribution.\n",
    "The result of this naive Gaussian assumption is shown in the following figure:\n",
    "\n",
    "创建一个简单模型的最快速方法就是假定数据服从一个两个维度之间没有协方差的正态分布。这个模型可以通过简单的寻找每个标签中点的均值和标准差来拟合，你只需要定义这个分布即可。高斯朴素假设的结果显示在下图中："
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![(run code in Appendix to generate image)](figures/05.05-gaussian-NB.png)\n",
    "[附录中生成图像的代码](06.00-Figure-Code.ipynb#Gaussian-Naive-Bayes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "> The ellipses here represent the Gaussian generative model for each label, with larger probability toward the center of the ellipses.\n",
    "With this generative model in place for each class, we have a simple recipe to compute the likelihood $P({\\rm features}~|~L_1)$ for any data point, and thus we can quickly compute the posterior ratio and determine which label is the most probable for a given point.\n",
    "\n",
    "上图中的椭圆表示每个标签的高斯生成模型，越接近椭圆中心位置具有越大的概率。有了每个分类的生成模型后，我们就能简单的计算每一个点的概率$P({\\rm features}~|~L_1)$，也就是后验概率，然后找到哪个标签在给定数据点上具有最大的概率。\n",
    "\n",
    "> This procedure is implemented in Scikit-Learn's ``sklearn.naive_bayes.GaussianNB`` estimator:\n",
    "\n",
    "这个过程在Scikit-Learn中实现成了`sklearn.naive_bayes.GaussianNB`评估器："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.naive_bayes import GaussianNB\n",
    "model = GaussianNB()\n",
    "model.fit(X, y);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Now let's generate some new data and predict the label:\n",
    "\n",
    "现在让我们创建一些新数据，然后预测标签："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "rng = np.random.RandomState(0)\n",
    "Xnew = [-6, -14] + [14, 18] * rng.rand(2000, 2)\n",
    "ynew = model.predict(Xnew)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Now we can plot this new data to get an idea of where the decision boundary is:\n",
    "\n",
    "下面我们将新数据点绘制在图上，你能看到判断的边界位置："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X[:, 0], X[:, 1], c=y, s=50, cmap='RdBu')\n",
    "lim = plt.axis()\n",
    "plt.scatter(Xnew[:, 0], Xnew[:, 1], c=ynew, s=20, cmap='RdBu', alpha=0.1)\n",
    "plt.axis(lim);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> We see a slightly curved boundary in the classifications—in general, the boundary in Gaussian naive Bayes is quadratic.\n",
    "\n",
    "我们看到分类之间的边界是有点弯曲的，通常来说，高斯朴素贝叶斯的边界是二次曲线。\n",
    "\n",
    "> A nice piece of this Bayesian formalism is that it naturally allows for probabilistic classification, which we can compute using the ``predict_proba`` method:\n",
    "\n",
    "这种贝叶斯分类方法的一个好处是它自然的支持概率分类，我们可以通过`predict_proba`计算每个分类的概率："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.89, 0.11],\n",
       "       [1.  , 0.  ],\n",
       "       [1.  , 0.  ],\n",
       "       [1.  , 0.  ],\n",
       "       [1.  , 0.  ],\n",
       "       [1.  , 0.  ],\n",
       "       [0.  , 1.  ],\n",
       "       [0.15, 0.85]])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yprob = model.predict_proba(Xnew)\n",
    "yprob[-8:].round(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> The columns give the posterior probabilities of the first and second label, respectively.\n",
    "If you are looking for estimates of uncertainty in your classification, Bayesian approaches like this can be a useful approach.\n",
    "\n",
    "上面结果中的两列分别给出了两个标签的后验概率。如果你在寻找你分类中的不确定性的话，贝叶斯方法能提供有用的判断依据。\n",
    "\n",
    "> Of course, the final classification will only be as good as the model assumptions that lead to it, which is why Gaussian naive Bayes often does not produce very good results.\n",
    "Still, in many cases—especially as the number of features becomes large—this assumption is not detrimental enough to prevent Gaussian naive Bayes from being a useful method.\n",
    "\n",
    "当然最终分类结果最多只能达到模型的假定情况，这表明高斯朴素贝叶斯方法常常不会产生非常好的结果。但是在很多情况下，特别是当特征数量变得很大时，这个假定并不会导致高斯朴素贝叶斯方法完全失去功能。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Multinomial Naive Bayes\n",
    "\n",
    "## 多项式朴素贝叶斯\n",
    "\n",
    "> The Gaussian assumption just described is by no means the only simple assumption that could be used to specify the generative distribution for each label.\n",
    "Another useful example is multinomial naive Bayes, where the features are assumed to be generated from a simple multinomial distribution.\n",
    "The multinomial distribution describes the probability of observing counts among a number of categories, and thus multinomial naive Bayes is most appropriate for features that represent counts or count rates.\n",
    "\n",
    "前面描述的高斯假设不是唯一的简单假设可以用来为每个标签产生生成分布。另一个有用的方法是多项式朴素贝叶斯，这个方法假定数据的特征是从一个简单的多项式分布中生成的。多项式分布描述了在一些分组中观察到的计数的概率，因此多项式朴素贝叶斯对于表达计数或计数的比例之类的特征是最合适的。\n",
    "\n",
    "> The idea is precisely the same as before, except that instead of modeling the data distribution with the best-fit Gaussian, we model the data distribuiton with a best-fit multinomial distribution.\n",
    "\n",
    "这里的原理和前面是一样的，只是不是使用正态分布来拟合数据模型，而是使用多项式分布来拟合数据模型。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Example: Classifying Text\n",
    "\n",
    "### 例子：分类文字\n",
    "\n",
    "> One place where multinomial naive Bayes is often used is in text classification, where the features are related to word counts or frequencies within the documents to be classified.\n",
    "We discussed the extraction of such features from text in [Feature Engineering](05.04-Feature-Engineering.ipynb); here we will use the sparse word count features from the 20 Newsgroups corpus to show how we might classify these short documents into categories.\n",
    "\n",
    "多项式朴素贝叶斯经常被用到的场合是文字分类，因为这个场景下的特征是单词的计数或者文档中单词出现的频率。我们在[特征工程](05.04-Feature-Engineering.ipynb)一节中介绍过在文本中提取这样的特征的方法；这里我们会使用20个新闻组的语料库提取出来的稀疏单词计数特征来展示将这些短文档分类的方法。\n",
    "\n",
    "> Let's download the data and take a look at the target names:\n",
    "\n",
    "让我们下载这个数据然后查看一下目标分类的名称："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Downloading 20news dataset. This may take a few minutes.\n",
      "Downloading dataset from https://ndownloader.figshare.com/files/5975967 (14 MB)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "['alt.atheism',\n",
       " 'comp.graphics',\n",
       " 'comp.os.ms-windows.misc',\n",
       " 'comp.sys.ibm.pc.hardware',\n",
       " 'comp.sys.mac.hardware',\n",
       " 'comp.windows.x',\n",
       " 'misc.forsale',\n",
       " 'rec.autos',\n",
       " 'rec.motorcycles',\n",
       " 'rec.sport.baseball',\n",
       " 'rec.sport.hockey',\n",
       " 'sci.crypt',\n",
       " 'sci.electronics',\n",
       " 'sci.med',\n",
       " 'sci.space',\n",
       " 'soc.religion.christian',\n",
       " 'talk.politics.guns',\n",
       " 'talk.politics.mideast',\n",
       " 'talk.politics.misc',\n",
       " 'talk.religion.misc']"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.datasets import fetch_20newsgroups\n",
    "\n",
    "data = fetch_20newsgroups()\n",
    "data.target_names"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> For simplicity here, we will select just a few of these categories, and download the training and testing set:\n",
    "\n",
    "这里为了简化，我们仅选择其中部分分类，然后载入训练集和测试集："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "categories = ['talk.religion.misc', 'soc.religion.christian',\n",
    "              'sci.space', 'comp.graphics']\n",
    "train = fetch_20newsgroups(subset='train', categories=categories)\n",
    "test = fetch_20newsgroups(subset='test', categories=categories)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Here is a representative entry from the data:\n",
    "\n",
    "下面是展示部分数据："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "From: dmcgee@uluhe.soest.hawaii.edu (Don McGee)\n",
      "Subject: Federal Hearing\n",
      "Originator: dmcgee@uluhe\n",
      "Organization: School of Ocean and Earth Science and Technology\n",
      "Distribution: usa\n",
      "Lines: 10\n",
      "\n",
      "\n",
      "Fact or rumor....?  Madalyn Murray O'Hare an atheist who eliminated the\n",
      "use of the bible reading and prayer in public schools 15 years ago is now\n",
      "going to appear before the FCC with a petition to stop the reading of the\n",
      "Gospel on the airways of America.  And she is also campaigning to remove\n",
      "Christmas programs, songs, etc from the public schools.  If it is true\n",
      "then mail to Federal Communications Commission 1919 H Street Washington DC\n",
      "20054 expressing your opposition to her request.  Reference Petition number\n",
      "\n",
      "2493.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(train.data[5])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> In order to use this data for machine learning, we need to be able to convert the content of each string into a vector of numbers.\n",
    "For this we will use the TF-IDF vectorizer (discussed in [Feature Engineering](05.04-Feature-Engineering.ipynb)), and create a pipeline that attaches it to a multinomial naive Bayes classifier:\n",
    "\n",
    "为了要将这个数据集应用到机器学习上，我们需要将数据中的每个字符串内容转换为数字的向量。我们使用TF-IDF来实现向量化（参见[特征工程](05.04-Feature-Engineering.ipynb)），然后创建一个管道操作将一个多项式朴素贝叶斯分类器连接进来："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "from sklearn.pipeline import make_pipeline\n",
    "\n",
    "model = make_pipeline(TfidfVectorizer(), MultinomialNB())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> With this pipeline, we can apply the model to the training data, and predict labels for the test data:\n",
    "\n",
    "我们可以将这个管道应用到训练集上，然后在测试集上去进行标签预测："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.fit(train.data, train.target)\n",
    "labels = model.predict(test.data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Now that we have predicted the labels for the test data, we can evaluate them to learn about the performance of the estimator.\n",
    "For example, here is the confusion matrix between the true and predicted labels for the test data:\n",
    "\n",
    "有了对测试数据预测的标签之后，我们可以对评估器的性能作出判断。例如下面展示了预测标签和实际标签之间的混淆矩阵："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "mat = confusion_matrix(test.target, labels)\n",
    "sns.heatmap(mat.T, square=True, annot=True, fmt='d', cbar=False,\n",
    "            xticklabels=train.target_names, yticklabels=train.target_names)\n",
    "plt.xlabel('true label')\n",
    "plt.ylabel('predicted label');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Evidently, even this very simple classifier can successfully separate space talk from computer talk, but it gets confused between talk about religion and talk about Christianity.\n",
    "This is perhaps an expected area of confusion!\n",
    "\n",
    "从上图看出，即便是这么简单的分类器也能成功的将宇宙学讨论和计算机科学讨论内容区分开，但是它在将宗教讨论和基督教讨论区分的时候遇到了困难。这可能是一个本来就容易混淆的领域。\n",
    "\n",
    "> The very cool thing here is that we now have the tools to determine the category for *any* string, using the ``predict()`` method of this pipeline.\n",
    "Here's a quick utility function that will return the prediction for a single string:\n",
    "\n",
    "我们现在有了一个工具用来对*任何*字符串进行分类检测了，非常酷对不对，在这个管道对象上使用`predict()`方法即可。下面我们创建一个简单的工具函数来对任何字符串输入返回标签预测的输出结果："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def predict_category(s, train=train, model=model):\n",
    "    pred = model.predict([s])\n",
    "    return train.target_names[pred[0]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Let's try it out:\n",
    "\n",
    "赶快来试一下："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'sci.space'"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predict_category('sending a payload to the ISS')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'soc.religion.christian'"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predict_category('discussing islam vs atheism')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'comp.graphics'"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predict_category('determining the screen resolution')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Remember that this is nothing more sophisticated than a simple probability model for the (weighted) frequency of each word in the string; nevertheless, the result is striking.\n",
    "Even a very naive algorithm, when used carefully and trained on a large set of high-dimensional data, can be surprisingly effective.\n",
    "\n",
    "请记住这里做的事情的复杂度仅是对字符串中每个单词的（加权）出现频率生成了一个概率模型而已；然而结果确令人惊奇。即使非常朴素的算法，只要小心使用，并且在一个大规模的高纬度数据集上进行训练的话，也能非常有效。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## When to Use Naive Bayes\n",
    "\n",
    "## 何时使用朴素贝叶斯方法\n",
    "\n",
    "> Because naive Bayesian classifiers make such stringent assumptions about data, they will generally not perform as well as a more complicated model.\n",
    "That said, they have several advantages:\n",
    "\n",
    "> - They are extremely fast for both training and prediction\n",
    "- They provide straightforward probabilistic prediction\n",
    "- They are often very easily interpretable\n",
    "- They have very few (if any) tunable parameters\n",
    "\n",
    "因为朴素贝叶斯分类器对数据进行了如此严格的假设，它们通常不会比其他复杂的模型更加有效。朴素贝叶斯方法有下面几个优点：\n",
    "\n",
    "- 它们非常快，无论是在训练还是预测中\n",
    "- 它们提供了直接的概率预测\n",
    "- 它们通常很容易解释\n",
    "- 它们有很少的可调参数\n",
    "\n",
    "> These advantages mean a naive Bayesian classifier is often a good choice as an initial baseline classification.\n",
    "If it performs suitably, then congratulations: you have a very fast, very interpretable classifier for your problem.\n",
    "If it does not perform well, then you can begin exploring more sophisticated models, with some baseline knowledge of how well they should perform.\n",
    "\n",
    "这些有点表明朴素贝叶斯分类器经常被作为初始化的基线分类标准。如果它性能很好，恭喜：你的问题已经有了一个非常快速很容易解释的分类模型了。如果它的性能不如人意，那么你可以开始尝试更加复杂的模型，然后将朴素贝叶斯分类器的性能结果作为标准来对新的模型进行评判。\n",
    "\n",
    "> Naive Bayes classifiers tend to perform especially well in one of the following situations:\n",
    "\n",
    "> - When the naive assumptions actually match the data (very rare in practice)\n",
    "- For very well-separated categories, when model complexity is less important\n",
    "- For very high-dimensional data, when model complexity is less important\n",
    "\n",
    "朴素贝叶斯分类器在下面的一些情况下通常能够特别良好的工作：\n",
    "\n",
    "- 当朴素假定能够拟合数据时（实践中非常少见）\n",
    "- 对于数据本身分类就已经很清晰的情况，此时模型复杂度并不十分重要\n",
    "- 对于数据维度非常多的情况，此时模型复杂度并不十分重要\n",
    "\n",
    "> The last two points seem distinct, but they actually are related: as the dimension of a dataset grows, it is much less likely for any two points to be found close together (after all, they must be close in *every single dimension* to be close overall).\n",
    "This means that clusters in high dimensions tend to be more separated, on average, than clusters in low dimensions, assuming the new dimensions actually add information.\n",
    "For this reason, simplistic classifiers like naive Bayes tend to work as well or better than more complicated classifiers as the dimensionality grows: once you have enough data, even a simple model can be very powerful.\n",
    "\n",
    "后两点看起来是独立的因素，但是实际上它们是关联的：当数据集的维度增加是，两个数据点非常接近的情况是非常少见的（毕竟它们要在*每个维度*都接近才能互相接近）。这意味着高纬度中的分类相对于低维度数据，会聚集在更不同的位置，如果新增的维度确实增加了数据的信息量（特征）的话。因此像朴素贝叶斯这样的简单分类器在数据维度增加情况下可能会比复杂分类器工作的更好：一旦你有了足够的数据，哪怕是简单的模型也能非常强大。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<!--NAVIGATION-->\n",
    "< [Feature Engineering](05.04-Feature-Engineering.ipynb) | [Contents](Index.ipynb) | [In Depth: Linear Regression](05.06-Linear-Regression.ipynb) >\n",
    "\n",
    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/05.05-Naive-Bayes.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
   ]
  }
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